What is Mixture of Experts?
Mixture of Experts (MoE) is a machine learning technique that embeds smaller “expert” networks inside a larger network. These experts are dynamically chosen for each input based on the specific task. MoE allows models to scale up accuracy and parameter counts while keeping FLOPS constant. It is a form of ensemble learning where the outputs of multiple models are combined, often leading to improved performance.
- With the same FLOPS, MoE can achieve much better accuracy than dense models. Google's Switch Transformer paper showed a 7x speedup to reach the same accuracy.
- MoE also outperforms larger dense models. Google showed a 2x speedup over a model with 3.5x more FLOPS.
- 128 experts significantly outperforms fewer experts or no experts given the same FLOPS budget.
- Each training example provides more benefit, so MoE helps when data is limited.
Despite being around since the 1990s, MoE has recently become more popular as model scaling through wider and deeper networks hits limitations. Companies like Google and Microsoft are rapidly solving MoE's challenges around complexity, training, and memory footprint. Challenges include:
- Larger expert counts increase overall parameters, which increases memory footprint.
- Dynamic routing leads to irregular communication patterns, which can reduce utilization.
- Fine-tuning and transfer learning can be problematic, but workarounds exist.
- Memory bandwidth bottlenecks limit the number of experts during inference.
How does Mixture of Experts work?
Mixture of Experts works by training multiple models on different portions of the input space. Each model becomes an "expert" on its specific portion. The outputs of these models are then combined, often using a gating network, to produce the final output.
- Training — Multiple models are trained on different portions of the input space.
- Combining — The outputs of the models are combined, often using a gating network, to produce the final output.
What are the applications of Mixture of Experts?
Mixture of Experts can be used in a wide range of machine learning tasks. These include regression, classification, and more complex tasks like image recognition and natural language processing.
- Regression — Mixture of Experts can be used for regression tasks, where the goal is to predict a continuous output variable.
- Classification — Mixture of Experts can be used for classification tasks, where the goal is to predict a categorical output variable.
- Image recognition — Mixture of Experts can be used for image recognition tasks, where the goal is to identify objects or features in images.
- Natural language processing — Mixture of Experts can be used for natural language processing tasks, where the goal is to understand and generate human language.
How is Mixture of Experts impacting AI?
Mixture of Experts is significantly impacting AI by enabling the development of more robust and accurate models. By combining the outputs of multiple models, Mixture of Experts often achieves better performance than any single model could. However, as with any machine learning technique, it is important to use Mixture of Experts responsibly to avoid issues around bias and transparency.
- Improved performance — Mixture of Experts often achieves better performance than any single model could.
- Robust models — By combining the outputs of multiple models, Mixture of Experts can create more robust models that are less likely to overfit to the training data.
- Responsible use — As with any machine learning technique, it is important to use Mixture of Experts responsibly to avoid issues around bias and transparency.
Mixture of Experts in Large Language Models
Google's 1.2 trillion parameter MoE model GLaM matched GPT-3 accuracy with 1/3 the energy and half the FLOPS. This shows MoE's efficiency benefits at scale. OpenAI's GPT-4 uses 16 experts. The dynamic routing leads to unpredictable batch sizes for each expert during inference. This causes latency and utilization issues. Inference optimization techniques like pruning underutilized experts can help maximize throughput.
GPT-4's Use of Mixture of Experts
GPT-4 utilizes a Mixture of Experts (MoE) architecture with 16 total experts. During each token generation, the routing algorithm selects 2 of the 16 experts to process the input and produce the output.
This provides two key benefits:
Scaling — With 16 separate experts that can be trained in parallel, GPT-4 can scale up to much larger sizes than previous dense transformer architectures.
Specialization — Each expert can specialize on particular types of inputs or tasks, improving overall accuracy. The routing algorithm learns to select the experts that will perform best for each input.
However, the dynamic routing of GPT-4's MoE architecture also introduces challenges:
Variable batch size — Because different experts are chosen per token, the batch size varies unpredictably for each expert. This leads to inconsistent latency and lower utilization.
Communication overhead — Routing inputs to different experts requires additional communication between GPUs/nodes, increasing overhead.
Memory bandwidth — More experts means memory bandwidth is a constraint, limiting the total number of experts.
To optimize GPT-4's MoE:
Pruning can remove underutilized experts to maximize throughput.
Careful expert placement minimizes communication between nodes.
Low-bandwidth algorithms like Top-2 gating reduce memory traffic.
MoE enables GPT-4 to scale up model size and specialization, at the cost of routing challenges that must be addressed by inference optimization techniques.